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Temporal Data Warehouses

  • Elzbieta Malinowski
  • Esteban Zimányi
Part of the Data-Centric Systems and Applications book series (DCSA)

Current data warehouse and OLAP models include a time dimension that, like other dimensions, is used for grouping purposes (using the roll-up operation) or in a predicate role (using the slice-and-dice operation). The time dimension also indicates the time frame for measures (for example, in order to know how many units of a product were sold in March 2007). However, the time dimension cannot be used to keep track of changes in other dimensions, for example, when a product changes its ingredients or its packaging. Consequently, the “nonvolatile" and “time-varying" features included in the definition of a data warehouse (Sect. 2.5) apply only to measures, and this situation leaves to applications the responsibility of representing changes in dimensions. Kimball et al. [147] proposed several solutions for this problem in the context of relational databases, the slowly changing dimensions. Nevertheless, these solutions are not satisfactory, since they either do not preserve the entire history of the data or are difficult to implement. Further, they do not take account of all research that has been done in the field of temporal databases.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Elzbieta Malinowski
    • 1
  • Esteban Zimányi
    • 2
  1. 1.School of Computer & Information ScienceUniversidad de Costa RicaSan Pedro, San JoséCosta Rica
  2. 2.Dept. of Computer Decision Engineering (CoDE)Université Libre BruxellesBruxellesBelgium

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